Data mining of customer choice behavior in internet of things within relationship network. (February 2020)

• Record Type: Journal Article
• Authors: Yan, Yuwei; Huang, Chuanchao; Wang, Qian; Hu, Bin
• Is Part Of: International journal of information management. Volume 50(2020)
• Page Start: 566
• Page End: 574
• Publication Date: 2020-02
• Subjects: Internet of things -- Customer relationship network -- Decision making -- Recommendation -- Fusion algorithm; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2018.11.013
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: We built a more complete customer relationship network based on personalized recommendations. Internet of Things has a certain effect in helping customers to make decisions and personalized recommendations. Our work show a better trend than the traditional algorithm in the accuracy and coverage. Abstract: Internet of Things has changed the relationship between traditional customer networks, and traditional information dissemination has been affected. Smart environment accelerates the changes in customer behaviors. Apparently, the new customer relationship network, benefitted from the Internet of Things technology, will imperceptibly influence customer choice behaviors for the cyber intelligence. In this work, we selected 298 customers' click browsing records as training data, and collected 50 customers who used the platform for the first time as research objects. and use the smart customer relationship network correspond to cyber intelligence to build the customer intelligence decision model in Internet of Things. The results showed that the MAE (Mean Absolute Deviation) of the customer trust evaluation model constructed in this study is 0.215, 45% improvement over the traditional equal assignment method. In addition, customer's consumer experience can be enhanced with the support of data mining technology in cyber intelligence. Our work indicated the key to build eliminates confusion in customer choice behavior mechanism is to establish a consumer-centric, effective network of customers and service providers, and to be supported by the Internet of Things, big data analysis, and relational fusion technologies.